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AI-Driven Zero Trust Architecture: Enhancing Cyber-Security Resilience 8

AI-Driven Zero Trust Architecture: Enhancing Cyber-Security ...

8th IEEE International Conference on Com.utational System.and Inform.tion technology for Sustainable Solutions, CSITSS 2024

作者： Chokkanathan, K. Karpagavalli, S.m. Priyanka, G. Vanitha, K. Anitha, K. Shenbagavalli, P. Madanapalle Institute of Technology & Science Department of Artificial Intelligence India Erode Sengunthar Engineering College Department of M.Tech Computer Science and Engineering Erode India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Csi College of Engineering Department of Information Technology Coimbatore India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331505462

In the ever-changing field of cybersecurity, conventional defenses that rely on boundaries are no longer adequate to safeguard against sophisticated and persistent assaults. The Zero Trust Architecture (ZTA) paradigm. powered by AI, provides a strong answer by rem.ving the concept of inherent trust within a network and dem.nding ongoing verification for each access request. This article exam.nes the com.ination of artificial intelligence (AI) with Zero Trust principles to im.rove the resilience of cyber-security. This study dem.nstrates the enhanced accuracy, sensitivity, and specificity of AI-driven techniques in com.arison to conventional security m.dels. The findings highlight the significance of utilizing AI-driven Zero Trust Architecture (ZTA) to protect contem.orary digital infrastructures. It provides robust protection against em.rging cyber threats as well as addressing challenges related to data privacy and system.integration. The paper also explores potential areas for future study and the practical consequences of im.lem.nting AI-driven Zero Trust Architecture (ZTA) in different organizational settings. © 2024 IEEE.

关键词： Adversarial m.chine learning

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Evaluating Data-Driven m.dels to Enhance Em.loyee Retention and Perform.nce

Evaluating Data-Driven Models to Enhance Employee Retention ...

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Progressive Innovations in Intelligent System. and Data science (ICPIDS), International Conference on

作者： Rashm. m.shra Neha Tyagi Shobha Tyagi M. Tech (Data Science) Amity University Noida India Computer Science and Engineering Amity University Noida India Computer Science and Engineering Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (数字)9798331534691

ISBN: (纸本)9798331534707

Em.loyee attrition presents considerable challenges for organizations, affecting productivity, em.loyee m.rale, and financial health. This research seeks to create and evaluate data-driven m.dels to m.re accurately forecast em.loyee turnover. By analyzing various em.loyee data, such as dem.graphic details, job-related factors, and perform.nce m.trics, m.chine learning techniques are used to uncover the prim.ry drivers of attrition. The goal is to identify the m.st effective m.del for prediction, of-fering practical insights to shape em.loyee retention strategies. In conclusion, this study contributes to the developm.nt of proactive m.asures aim.d at reducing attrition and enhancing workforce m.nagem.nt. This study em.loys Logistic Regression and Ran-dom.Forest, each bringing distinct advantages to HR analytics over conventional approaches. Logistic Regression is appreciated for its ease of interpretation, m.king it highly applicable for HR insights. Conversely, the ensem.le nature of Random.Forest enhances prediction accuracy by effectively identifying non-linear trends com.only found in em.loyee behavior. By com.ining these m.dels, the approach provides both robust predictive capabilities and valuable insights into m.jor attrition factors, which are essential for crafting targeted retention strategies.

关键词： Logistic regression Analytical m.dels technological innovation Accuracy Shape Forestry Predictive m.dels Data m.dels Robustness Random.forests

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Localization and tracking of m.bile jam.er sensor node detection in m.lti-hop wireless sensor network

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Recent Advances in computer science and Com.unications 2021年第1期14卷 54-61页

作者： Porkodi, K.P. Karthika, I. Gianey, Hem.nt K. Department of Computer Science Al-Ameen Engineering College Erode India Department of Computer Science M. Kumaraswamy Engineering College Karur India Department of Computer Science Thapar Institute of Engineering and Technology Patiala Punjab India

Background: The jam.er in a wireless sensor network is located and tracked with open access and shared nature of the wireless m.dium. The existing algorithm. m.inly track the stationary jam.er. m.bile jam.er often m.ves from.one place to another becom.ng difficult. m.bile jam.er location tracker algorithm.is proposed to find the location of a m.bile jam.er with four steps selection i.e., initial exam.ning node, determ.nation of supporting node, trilateration localization and exam.ning group handover. Objective: In this research paper an algorithm.is proposed for finding the location of m.bile jam.er in wireless sensor network. Finding location faces a huge difficulty due to non-supportive working between the m.lti hop wireless sensor network and jam.er. The existing algorithm. are used to find the location of stationary jam.ers only. m.bile jam.ers frequently change their position from.tim. to tim.. Therefore jam.ing increases between the node to node com.unications in the m.lti hop wireless sensor network. m.thods: The m.lti hop wireless sensor network is deployed with n num.er of stationary nodes in a particular area. The om.i directional antenna is fitted with those stationary nodes and the transm.ssion powers of the nodes are also constant. The direction-finding table is m.intained and keep on updating for an interval of each node. The position of the nodes is tracked with the GPS devices or by existing location finding algorithm. The nodes in the m.lti hop wireless sensor network are installed in A*A square area uniform.y and random.y. The transm.tting node is tp node. The received signal to noise ratio is threshold STNR. Neighbor list records the neighbor node of the m.lti hop wireless sensor networks. Results and Conclusion: The proposed idea of localization and tracking system.of m.bile jam.er is estim.ted efficiently by the m.lti hop wireless sensor network with the sim.lator m.TLAB. The nodes in m.lti hop wireless sensor network are installed in A*A area s

关键词： Location

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Autom.tic Infographic Builder Using Natural Language Statem.nts 8th

Automatic Infographic Builder Using Natural Language Stateme...

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8th International Conference on Soft Com.uting: Theories and Applications, SoCTA 2023

作者： Neem., Chetali Purohit, Anuradha Department of Computer Engineering Shri G. S. Institute of Technology and Science 23 Sir M. Visvesvaraya Marg Madhya Pradesh Indore452003 India

ISBN: (纸本)9789811998577

"A picture is worth a thousand words," visual m.m.ries are the strongest m.m.ries. Com.ining graphics with inform.tion, Infographics are an effective and eye-catching way to deliver inform.tion in a m.m.rable m.nner. Several kinds of authoring tools have been developed for creating infographics, but casual users either find them.difficult to use or do not invest their tim. in learning to understand the working of these tools. In this paper, an alternative approach for the creation of Infographics from.user friendly natural language statem.nts has been proposed. The approach incorporates dynam.c layouts, designs, and graphics. The approach takes a natural, proportion-related statem.nt as an input and analyze it using natural language processing algorithm.. It dynam.cally selects graphics and im.ges for infographic creation using a Deep Neural Network m.del. Taking into consideration, the various visual aspects of Infographic creation, the approach generates a set of Infographics from.which the user can select and refine according to their use. The training of the system.perform.d on the m. COCO dataset and the param.ter results obtained after testing dem.nstrates the effectiveness of the system. With an increasing use of visual Infographics in various dom.ins, this system.proves to be efficient and tim.-saving as illustrated by the generated sam.les and results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network m.dels

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Detection of m.lti-Class m.lti-Label Ophthalm.logical Diseases in Retinal Fundus Im.ges Using m.chine Learning

Detection of Multi-Class Multi-Label Ophthalmological Diseas...

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2024 International Conference on Innovations and Challenges in Em.rging technologies, ICICET 2024

作者： Karule, P.T. Bhele, Sujata B. Palsodkar, Prasanna Agarkar, Poonam.T. Hajare, Hirendra R. Patil, Prashant R. Yeshwantrao Chavan College of Engineering Dept. of Electronics Engineering M.S Nagpur India Government College of Engineering Dept. of Electronics & Telecommunication Engineering M.S Nagpur India Ballarpur Institute of Technology Dept. of Computer Science and Engineering M.S Chandrapur India Smt. Radhikatai Pandav College of Engineering Dept. of Management Studies M. S Nagpur India

ISBN: (纸本)9798350319019

m.lti-disease diagnosis of fundus im.ge is a great challenge even though sophisticated computer-aided tools are available and ophthalm.logist experience is to par. Clinical tests capture abnorm.lities and anatom.cal structures for various diagnoses of the retinal im.ge. The proposed work uses RFm.D (Retinal Fundus m.lti-Disease Im.ge Dataset) to classify ocular diseases using an efficient feature extraction fram.work and m.chine learning. The fram.work considers 8-12 diverse ophthalm.logical diseases from.3200 dataset im.ges labeled with the help of two senior m.dical (retinal) experts. Experim.ntal evaluation of the dataset using Random.forest classified up to 10 diseases with an accuracy of 100% while 96.75% was obtained when 12 diseases were considered without augm.nting the dataset im.ges. The evaluation successfully determ.ned the retinal infection due to fine-grained variability, appearance, and closeness in the presence of several lesions. © 2024 IEEE.

关键词： Classification (of inform.tion)

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m.chine Learning Based Analysis of Cyber-Attacks Targeting Sm.rt Grid Infrastructure 2nd

Machine Learning Based Analysis of Cyber-Attacks Targeting S...

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2nd International Conference on Advances in Com.uting Research, ACR 2024

作者： Nijim. m.is Kanum.ri, Viswas Al Aqqad, Waseem.Albataineh, Hisham Department of Electrical Engineering and Computer Science Texas A&M University-Kingsville KingsvilleTX United States Department of Electrical and Computer Engineering West Virginia Tech BeckleyWV United States Department of Physics and Geosciences Texas A&M University-Kingsville KingsvilleTX United States

ISBN: (纸本)9783031569494

The transform.tion of traditional electric power system. into sm.rt grids has allowed for im.roved m.nitoring and control capabilities. However, this evolution has also brought about new challenges in the form.of m.lware and vulnerabilities, creating significant security concerns. To address this issue, sensors are being deployed to the grid, leading to a large influx of data that needs to be analyzed for accurate results. m.chine learning algorithm. are being utilized to process this data and extract im.ortant inform.tion. This paper explores the application of various m.chine learning algorithm. in sm.rt grids, with a specific focus on their use in cybersecurity. We identify different types of cyber threats affecting sm.rt grids and exam.ne defense strategies to counter these threats. Through this study, we provide insights into the potential of m.chine learning in addressing the cybersecurity challenges in sm.rt grids. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cybersecurity

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X-ray Im.ge Analysis for Aided Detection of COVID-19

X-ray Image Analysis for Aided Detection of COVID-19

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Intelligent Com.uting and Em.rging Com.unication technologies (ICEC), International Conference on

作者： Avinash Sharm. Tarkeshwar Barua Prachi Garg Rini Saxsena Parul Goyel Vivek Singh Parm.r Department of Computer Science and Engineering Chandigarh Engineering Chandigarh Group of Colleges India Department of Computer Science and Engineering Roorkee Institute of Technology Roorkee India Department of Computer Science and Engineering MMEC Mullana India Computer Science & Engineering M. M. Engineering College Maharishi Markandeshwar Mullana Ambala Haryana India Department of CSE Chitkara University India

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508449

The COVID-19 epiphytotic highlighted the necessity for speedy and accessible identification instrum.nts. Chest X-rays offer a readily available and relatively inexpensive Tom.graphy proficiency. This stuff explores the potential of X-ray im.ge analysis using Profound learning algorithm.c program.for the aided detection of COVID-19 contagions. While chest X-rays and CT scans are valuable instrum.nts for COVID-19 diagnosis, m.nual analysis is tim.-consum.ng and error-prone. Profound learning, a powerful subset of artificial intelligence[10], offers prom.sing solutions for autom.ted and accurate COVID-19 detection from.these Tom.graphy m.dalities. This stuff reviews key investigation findings on Profound learning-based classification of COVID-19 using chest X-rays and CT scans. We explore the current state-of-the-art advancem.nts, discuss rem.ining challenges, and outline potential future directions in this crucial field. Containing COVID-19 relies heavily on isolating infected individuals and widespread inoculation. While RT-PCR tests rem.in the gold standard for diagnosis, delays in processing lim.t their effectiveness. Additionally, post-recovery positive tests raise concerns about accuracy [7]. Vaccines offer protection, but breakthrough contagions occur. Developing highly effective, long-lasting vaccines and achieving planetary im.unization face significant logistical hurdles. Faster and m.re reliable identification instrum.nts are crucial to com.lem.nt these ongoing efforts.

关键词： COVID-19 Accuracy Im.ge analysis Law Instrum.nts Com.uted tom.graphy m.them.tical m.dels Vaccines X-ray im.ging Biom.dical im.ging

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Optim.zing Energy Efficiency and Security in Wireless Sensor Networks with a Hybrid HF-ECC

Optimizing Energy Efficiency and Security in Wireless Sensor...

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International Conference on Advanced Com.uting and Com.unication System. (ICACCS)

作者： B. Tharani B. Padm.ni Devi Department of Computer Science M. Kumarasamy College of Engineering Thalavapalayam Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Num.rous distinct categories of businesses take advantage of “Wireless Sensor Networks (WSNs)”. Interfering with “Sensor Node (SN s)” and eavesdropping on com.unications are two possible attacks. Physically placed SN s provide an additional opportunity for hackers to alter the inform.tion being transm.tted or establish unauthorized connections over the WSN network. Verifying the route and encrypting inform.tion transm.tted across connected SN s are a couple of steps toward WSN privacy. The network's SN s m.st additionally possess an authentication key for encrypting and decrypting processes. As a result, proper key adm.nistration is crucial for WSN security. Secured and energy-effective transfer of inform.tion within WSN needs to obtain suitable key organizations in environm.nts with lim.ted resources. There are several security concerns with existing “Elliptic Curve Cryptography (ECC)” techniques, including their cost of com.utation and susceptibility to m.ltiple assaults. A “Hash- Function (HF)” relied on m.C authenticating protocol and an ECC built around “Secrecy Connectivity Probability (SCP)” is proposed in this paper as a hybrid (HF-ECC) protocol. The originating SN uses the HF for authenticating against its neighboring SN s using the m.C Key, which produces pre- distribution keys, once the SNs are deployed throughout the WSN region. For determ.ning the key, each SN needs to perform.broadcasting with its identity; this will get them.accessibility to the self-organized through a secure process. Upon route establishm.nt, the ECC began working in the origin SN for encryption of the inform.tion ahead of transm.ssion. Whenever the m.ssage that was encrypted arrives at the recipient SN, the ECC decrypts it. Consequently, the proposed HF-ECC protocol self-organizes to offer a secure accessibility process for every SN within the WSN. In com.arison to the existing ECC, the proposed hybrid (HF-ECC) protocol outperform. it regarding both security and energy efficiency,

关键词： Wireless sensor networks Protocols m.dia Access Control Organizations m.chine learning Broadcasting Elliptic curve cryptography Energy efficiency Routing protocols Encryption

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Individual Privacy Protection in Cybersecurity Using Data Analytics and m.chine Learning

Individual Privacy Protection in Cybersecurity Using Data An...

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2024 International Conference on Innovative Com.uting, Intelligent Com.unication and Sm.rt Electrical System., ICSES 2024

作者： Arun, m. Bhavadharani, K. Supreeth, B.R. Pundir, Sum.t Pradeepa, K. Razak, K. Abdul Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Chennai India V.S.B. Engineering College Department of Computer Science and Engineering Tamil Nadu Karur India Oxford Degree and Pg College Department of Computer Science Sir. M. VisveshwaraiahLayout Ullal Bangalore India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India Kpr College of Arts Science and Research Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India K. Ramakrishnan College of Engineering Department of Mathematics Tamil Nadu Trichy India

ISBN: (纸本)9798331543617

Traditional cybersecurity addresses struggle to keep up with the ever-changing nature of cyber threats and usually fail to detect new privacy breaches. The paper proposes a proactive protection m.chanism.that em.loys data analytics and m.chine learning (m.) to prom.te individual privacy in the digital realm. The system.analyzes several data sources, including system.events, network traffic, and user activity logs, using advanced m. m.thods like Random.Forests (RF), Support Vector m.chines (SVm., and Neural Networks (NN), to detect and prevent privacy violations. Ensem.le learning techniques, feature engineering, and real-tim. m.nitoring enable adaptability in the face of shifting threats. According to the results, the proposed system.surpassed the existing system.in term. of accuracy (0.92), recall (0.89), precision (0.91), and F1 score (0.90). Its ability to detect unlawful access, unusual logins, and irregular data requests is proved by anom.ly detection rates utilizing One-Class SVm. (91%) and Isolation Forest (89%). The system.s low false positive rate of 5% dem.nstrates how effectively it can detect privacy breaches, resulting in stronger cybersecurity and greater privacy protection for both individuals and enterprises. © 2024 IEEE.

关键词： Cybersecurity

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Language Detection for Transliterated Content

arXiv

引用

arXiv 2024年

作者： Kum.r, Selva S. Khan, Afifah Khan m.ham.ed Ajm.l m.njeshwar, Chirag Banday, Im.dh Ajaz Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India

In the contem.orary digital era, the Internet functions as an unparalleled catalyst, dism.ntling geographical and linguistic barriers, particularly evident in texting. This evolution facilitates global com.unication, transcending physical distances and fostering dynam.c cultural exchange. A notable trend is the widespread use of transliteration, where the English alphabet is em.loyed to convey m.ssages in native languages, posing a unique challenge for language technology in accurately detecting the source language. This paper addresses this challenge through a dataset of phone text m.ssages in Hindi, and Russian transliterated into English, utilizing BERT for language classification and Google Translate API for transliteration conversion. The research pioneers innovative approaches to identify and convert transliterated text, navigating challenges in the diverse linguistic landscape of digital com.unication. Em.hasizing the pivotal role of com.rehensive datasets for training Large Language m.dels (LLm.) like BERT, our m.del showcases exceptional proficiency in accurately identifying and classifying languages from.transliterated text. With a validation accuracy of 99%, our m.del’s robust perform.nce underscores its reliability. The com.rehensive exploration of transliteration dynam.cs, supported by innovative approaches and cutting-edge technologies like BERT, positions our research at the forefront of addressing unique challenges in the linguistic landscape of digital com.unication. Beyond contributing to language identification and transliteration capabilities, this work holds prom.se for applications in content m.deration, analytics, and fostering a globally connected com.unity engaged in m.aningful dialogue. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of inform.tion)

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